Overview

Dataset statistics

Number of variables26
Number of observations171150
Missing cells8287
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 MiB
Average record size in memory208.0 B

Variable types

Numeric6
Text12
Unsupported3
Categorical2
DateTime3

Alerts

ACCESSORIAL_AMOUNT is highly overall correlated with AMOUNT_PAIDHigh correlation
AMOUNT_PAID is highly overall correlated with ACCESSORIAL_AMOUNTHigh correlation
IO_CODE is highly overall correlated with Unnamed: 0High correlation
Unnamed: 0 is highly overall correlated with IO_CODEHigh correlation
DESTINATION_COUNTRY_CODE has 1954 (1.1%) missing valuesMissing
ORIGIN_STATE has 3125 (1.8%) missing valuesMissing
ACCESSORIAL_AMOUNT is highly skewed (γ1 = 58.23637883)Skewed
AMOUNT_PAID is highly skewed (γ1 = 39.80553388)Skewed
SHIP_WEIGHT is highly skewed (γ1 = 34.1117575)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
DESTINATION_CITY is an unsupported type, check if it needs cleaning or further analysisUnsupported
DESTINATION_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGIN_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ACCESSORIAL_AMOUNT has 15279 (8.9%) zerosZeros
MILEAGE has 37398 (21.9%) zerosZeros
SHIP_WEIGHT has 19900 (11.6%) zerosZeros

Reproduction

Analysis started2024-02-16 07:11:30.939693
Analysis finished2024-02-16 07:11:49.965448
Duration19.03 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct171150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85574.5
Minimum0
Maximum171149
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:50.063134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8557.45
Q142787.25
median85574.5
Q3128361.75
95-th percentile162591.55
Maximum171149
Range171149
Interquartile range (IQR)85574.5

Descriptive statistics

Standard deviation49406.894
Coefficient of variation (CV)0.57735533
Kurtosis-1.2
Mean85574.5
Median Absolute Deviation (MAD)42787.5
Skewness0
Sum1.4646076 × 1010
Variance2.4410411 × 109
MonotonicityStrictly increasing
2024-02-15T23:11:50.239793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
114093 1
 
< 0.1%
114095 1
 
< 0.1%
114096 1
 
< 0.1%
114097 1
 
< 0.1%
114098 1
 
< 0.1%
114099 1
 
< 0.1%
114100 1
 
< 0.1%
114101 1
 
< 0.1%
114102 1
 
< 0.1%
Other values (171140) 171140
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
171149 1
< 0.1%
171148 1
< 0.1%
171147 1
< 0.1%
171146 1
< 0.1%
171145 1
< 0.1%
171144 1
< 0.1%
171143 1
< 0.1%
171142 1
< 0.1%
171141 1
< 0.1%
171140 1
< 0.1%

ACCESSORIAL_AMOUNT
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct41990
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768.96844
Minimum0
Maximum896151.99
Zeros15279
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:50.446775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.07656
median57.99996
Q3286.33374
95-th percentile2509.6217
Maximum896151.99
Range896151.99
Interquartile range (IQR)265.25718

Descriptive statistics

Standard deviation6680.9729
Coefficient of variation (CV)8.6882277
Kurtosis5461.2501
Mean768.96844
Median Absolute Deviation (MAD)46.8441
Skewness58.236379
Sum1.3160895 × 108
Variance44635398
MonotonicityNot monotonic
2024-02-15T23:11:50.592097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15279
 
8.9%
68.328 2037
 
1.2%
91.98 1352
 
0.8%
51.7716 819
 
0.5%
65.7 705
 
0.4%
328.5 385
 
0.2%
131.4 373
 
0.2%
64.55682 362
 
0.2%
459.9 355
 
0.2%
19.71 342
 
0.2%
Other values (41980) 149141
87.1%
ValueCountFrequency (%)
0 15279
8.9%
0.01314 2
 
< 0.1%
0.07884 1
 
< 0.1%
1.0512 1
 
< 0.1%
1.15632 1
 
< 0.1%
1.26144 3
 
< 0.1%
1.314 2
 
< 0.1%
1.52424 1
 
< 0.1%
1.53738 1
 
< 0.1%
1.5768 1
 
< 0.1%
ValueCountFrequency (%)
896151.9946 1
< 0.1%
846140.4319 1
< 0.1%
655493.9589 1
< 0.1%
592760.6161 1
< 0.1%
563688.5107 1
< 0.1%
533514.695 1
< 0.1%
448882.9772 1
< 0.1%
419682.4677 1
< 0.1%
392183.01 1
< 0.1%
353885.4945 1
< 0.1%

AMOUNT_PAID
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct66507
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2234.2118
Minimum-182.33064
Maximum1248300
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size1.3 MiB
2024-02-15T23:11:50.725850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-182.33064
5-th percentile100.3239
Q1169.12494
median394.24599
Q31553.3845
95-th percentile7667.2695
Maximum1248300
Range1248482.3
Interquartile range (IQR)1384.2596

Descriptive statistics

Standard deviation12427.793
Coefficient of variation (CV)5.5624952
Kurtosis2464.4414
Mean2234.2118
Median Absolute Deviation (MAD)277.08318
Skewness39.805534
Sum3.8238536 × 108
Variance1.5445003 × 108
MonotonicityNot monotonic
2024-02-15T23:11:50.887909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.98 1329
 
0.8%
51.7716 816
 
0.5%
1859.31 564
 
0.3%
65.7 544
 
0.3%
2168.1 488
 
0.3%
657 382
 
0.2%
64.55682 360
 
0.2%
854.1 322
 
0.2%
96.40818 321
 
0.2%
96.579 289
 
0.2%
Other values (66497) 165735
96.8%
ValueCountFrequency (%)
-182.33064 1
 
< 0.1%
-20.31444 1
 
< 0.1%
0.01314 1
 
< 0.1%
0.07884 1
 
< 0.1%
1.314 3
< 0.1%
1.39284 2
< 0.1%
1.49796 1
 
< 0.1%
1.53738 1
 
< 0.1%
1.74762 1
 
< 0.1%
1.7739 1
 
< 0.1%
ValueCountFrequency (%)
1248300 1
 
< 0.1%
1069405.47 1
 
< 0.1%
934680.958 1
 
< 0.1%
896151.9946 1
 
< 0.1%
846140.4319 1
 
< 0.1%
802120.1836 1
 
< 0.1%
770943.51 5
< 0.1%
700061.1071 1
 
< 0.1%
657000 4
< 0.1%
655493.9589 1
 
< 0.1%
Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:51.073656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length20.21303
Min length7

Characters and Unicode

Total characters3459460
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowHodges, Lyons and Weaver
2nd rowHodges, Lyons and Weaver
3rd rowHodges, Lyons and Weaver
4th rowHodges, Lyons and Weaver
5th rowHodges, Lyons and Weaver
ValueCountFrequency (%)
and 110852
21.3%
hodges 58917
11.3%
weaver 58917
11.3%
lyons 58917
11.3%
brown 16042
 
3.1%
duffy 15782
 
3.0%
singh 15782
 
3.0%
anderson-esparza 15438
 
3.0%
foster 14862
 
2.8%
peterson 14862
 
2.8%
Other values (128) 141230
27.1%
2024-02-15T23:11:51.353061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
350451
 
10.1%
e 337300
 
9.8%
n 319555
 
9.2%
a 275899
 
8.0%
o 267911
 
7.7%
s 231039
 
6.7%
r 230344
 
6.7%
d 209604
 
6.1%
, 110634
 
3.2%
y 92755
 
2.7%
Other values (41) 1033968
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2499102
72.2%
Uppercase Letter 457088
 
13.2%
Space Separator 350451
 
10.1%
Other Punctuation 110634
 
3.2%
Dash Punctuation 42185
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 337300
13.5%
n 319555
12.8%
a 275899
11.0%
o 267911
10.7%
s 231039
9.2%
r 230344
9.2%
d 209604
8.4%
y 92755
 
3.7%
g 89799
 
3.6%
v 58994
 
2.4%
Other values (16) 385902
15.4%
Uppercase Letter
ValueCountFrequency (%)
L 87463
19.1%
W 68000
14.9%
H 64344
14.1%
P 27497
 
6.0%
B 22674
 
5.0%
T 22088
 
4.8%
A 20627
 
4.5%
D 18660
 
4.1%
E 18286
 
4.0%
I 16749
 
3.7%
Other values (12) 90700
19.8%
Space Separator
ValueCountFrequency (%)
350451
100.0%
Other Punctuation
ValueCountFrequency (%)
, 110634
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2956190
85.5%
Common 503270
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 337300
11.4%
n 319555
 
10.8%
a 275899
 
9.3%
o 267911
 
9.1%
s 231039
 
7.8%
r 230344
 
7.8%
d 209604
 
7.1%
y 92755
 
3.1%
g 89799
 
3.0%
L 87463
 
3.0%
Other values (38) 814521
27.6%
Common
ValueCountFrequency (%)
350451
69.6%
, 110634
 
22.0%
- 42185
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3459460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
350451
 
10.1%
e 337300
 
9.8%
n 319555
 
9.2%
a 275899
 
8.0%
o 267911
 
7.7%
s 231039
 
6.7%
r 230344
 
6.7%
d 209604
 
6.1%
, 110634
 
3.2%
y 92755
 
2.7%
Other values (41) 1033968
29.9%

DESTINATION_CITY
Unsupported

REJECTED  UNSUPPORTED 

Missing191
Missing (%)0.1%
Memory size1.3 MiB
Distinct2479
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:51.553216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length12.299112
Min length1

Characters and Unicode

Total characters2104993
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)0.5%

Sample

1st rowFAIRBURN, GA
2nd rowFAIRBURN, GA
3rd rowFAIRBURN, GA
4th rowFAIRBURN, GA
5th rowFAIRBURN, GA
ValueCountFrequency (%)
ca 78949
18.9%
san 58387
14.0%
diego 57713
13.8%
tx 38537
 
9.2%
ga 16070
 
3.8%
fairburn 14308
 
3.4%
xx 12997
 
3.1%
desoto 12561
 
3.0%
mabank 8123
 
1.9%
houston 6519
 
1.6%
Other values (2339) 114055
27.3%
2024-02-15T23:11:51.881909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 257755
12.2%
247117
11.7%
, 171152
 
8.1%
O 159409
 
7.6%
N 152881
 
7.3%
E 139652
 
6.6%
I 108184
 
5.1%
S 102105
 
4.9%
C 99398
 
4.7%
G 93837
 
4.5%
Other values (28) 573503
27.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1686681
80.1%
Space Separator 247117
 
11.7%
Other Punctuation 171153
 
8.1%
Open Punctuation 13
 
< 0.1%
Decimal Number 11
 
< 0.1%
Currency Symbol 9
 
< 0.1%
Dash Punctuation 8
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 257755
15.3%
O 159409
 
9.5%
N 152881
 
9.1%
E 139652
 
8.3%
I 108184
 
6.4%
S 102105
 
6.1%
C 99398
 
5.9%
G 93837
 
5.6%
D 88078
 
5.2%
T 85549
 
5.1%
Other values (16) 399833
23.7%
Decimal Number
ValueCountFrequency (%)
0 4
36.4%
9 3
27.3%
7 2
18.2%
1 1
 
9.1%
5 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 171152
> 99.9%
. 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
247117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1686681
80.1%
Common 418312
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 257755
15.3%
O 159409
 
9.5%
N 152881
 
9.1%
E 139652
 
8.3%
I 108184
 
6.4%
S 102105
 
6.1%
C 99398
 
5.9%
G 93837
 
5.6%
D 88078
 
5.2%
T 85549
 
5.1%
Other values (16) 399833
23.7%
Common
ValueCountFrequency (%)
247117
59.1%
, 171152
40.9%
( 13
 
< 0.1%
$ 9
 
< 0.1%
- 8
 
< 0.1%
0 4
 
< 0.1%
9 3
 
< 0.1%
7 2
 
< 0.1%
) 1
 
< 0.1%
1 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2104993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 257755
12.2%
247117
11.7%
, 171152
 
8.1%
O 159409
 
7.6%
N 152881
 
7.3%
E 139652
 
6.6%
I 108184
 
5.1%
S 102105
 
4.9%
C 99398
 
4.7%
G 93837
 
4.5%
Other values (28) 573503
27.2%
Distinct98
Distinct (%)0.1%
Missing1954
Missing (%)1.1%
Memory size1.3 MiB
2024-02-15T23:11:52.015613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters338392
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 154817
91.5%
cz 6576
 
3.9%
be 2032
 
1.2%
au 1453
 
0.9%
ca 638
 
0.4%
de 623
 
0.4%
cn 410
 
0.2%
gb 394
 
0.2%
uk 195
 
0.1%
mx 163
 
0.1%
Other values (88) 1895
 
1.1%
2024-02-15T23:11:52.243072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 156541
46.3%
S 155005
45.8%
C 7737
 
2.3%
Z 6763
 
2.0%
E 2883
 
0.9%
B 2513
 
0.7%
A 2250
 
0.7%
D 851
 
0.3%
G 684
 
0.2%
N 652
 
0.2%
Other values (16) 2513
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 338392
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 156541
46.3%
S 155005
45.8%
C 7737
 
2.3%
Z 6763
 
2.0%
E 2883
 
0.9%
B 2513
 
0.7%
A 2250
 
0.7%
D 851
 
0.3%
G 684
 
0.2%
N 652
 
0.2%
Other values (16) 2513
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 338392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 156541
46.3%
S 155005
45.8%
C 7737
 
2.3%
Z 6763
 
2.0%
E 2883
 
0.9%
B 2513
 
0.7%
A 2250
 
0.7%
D 851
 
0.3%
G 684
 
0.2%
N 652
 
0.2%
Other values (16) 2513
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 156541
46.3%
S 155005
45.8%
C 7737
 
2.3%
Z 6763
 
2.0%
E 2883
 
0.9%
B 2513
 
0.7%
A 2250
 
0.7%
D 851
 
0.3%
G 684
 
0.2%
N 652
 
0.2%
Other values (16) 2513
 
0.7%
Distinct5611
Distinct (%)3.3%
Missing191
Missing (%)0.1%
Memory size1.3 MiB
2024-02-15T23:11:52.415147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length13.572354
Min length7

Characters and Unicode

Total characters2320316
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2927 ?
Unique (%)1.7%

Sample

1st rowMichael Clark
2nd rowMichael Clark
3rd rowMichael Clark
4th rowMichael Clark
5th rowMichael Clark
ValueCountFrequency (%)
christopher 28768
 
8.4%
shepard 27988
 
8.1%
brown 19137
 
5.6%
ryan 18963
 
5.5%
ashley 17966
 
5.2%
cruz 17554
 
5.1%
michael 13222
 
3.8%
clark 11221
 
3.3%
carl 9452
 
2.7%
ramsey 9430
 
2.7%
Other values (1478) 170234
49.5%
2024-02-15T23:11:52.740139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 207012
 
8.9%
e 204964
 
8.8%
a 175021
 
7.5%
172976
 
7.5%
h 156406
 
6.7%
i 128861
 
5.6%
l 126649
 
5.5%
n 117738
 
5.1%
o 112085
 
4.8%
s 102777
 
4.4%
Other values (44) 815827
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1800902
77.6%
Uppercase Letter 345618
 
14.9%
Space Separator 172976
 
7.5%
Other Punctuation 820
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 207012
11.5%
e 204964
11.4%
a 175021
9.7%
h 156406
8.7%
i 128861
 
7.2%
l 126649
 
7.0%
n 117738
 
6.5%
o 112085
 
6.2%
s 102777
 
5.7%
y 86339
 
4.8%
Other values (16) 383050
21.3%
Uppercase Letter
ValueCountFrequency (%)
C 82345
23.8%
R 41890
12.1%
S 39509
11.4%
M 26218
 
7.6%
A 24922
 
7.2%
B 24016
 
6.9%
J 14843
 
4.3%
T 13127
 
3.8%
D 11513
 
3.3%
P 9370
 
2.7%
Other values (16) 57865
16.7%
Space Separator
ValueCountFrequency (%)
172976
100.0%
Other Punctuation
ValueCountFrequency (%)
. 820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2146520
92.5%
Common 173796
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 207012
 
9.6%
e 204964
 
9.5%
a 175021
 
8.2%
h 156406
 
7.3%
i 128861
 
6.0%
l 126649
 
5.9%
n 117738
 
5.5%
o 112085
 
5.2%
s 102777
 
4.8%
y 86339
 
4.0%
Other values (42) 728668
33.9%
Common
ValueCountFrequency (%)
172976
99.5%
. 820
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2320316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 207012
 
8.9%
e 204964
 
8.8%
a 175021
 
7.5%
172976
 
7.5%
h 156406
 
6.7%
i 128861
 
5.6%
l 126649
 
5.5%
n 117738
 
5.1%
o 112085
 
4.8%
s 102777
 
4.4%
Other values (44) 815827
35.2%
Distinct113
Distinct (%)0.1%
Missing989
Missing (%)0.6%
Memory size1.3 MiB
2024-02-15T23:11:52.900219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9999941
Min length1

Characters and Unicode

Total characters340321
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowGA
2nd rowGA
3rd rowGA
4th rowGA
5th rowGA
ValueCountFrequency (%)
ca 78946
46.4%
tx 38537
22.6%
ga 16070
 
9.4%
xx 12997
 
7.6%
ok 4078
 
2.4%
la 3216
 
1.9%
az 2229
 
1.3%
pa 984
 
0.6%
il 970
 
0.6%
cz 919
 
0.5%
Other values (103) 11215
 
6.6%
2024-02-15T23:11:53.252207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 103032
30.3%
C 81360
23.9%
X 64590
19.0%
T 39393
 
11.6%
G 16146
 
4.7%
O 5996
 
1.8%
L 4815
 
1.4%
K 4717
 
1.4%
Z 3159
 
0.9%
N 2789
 
0.8%
Other values (17) 14324
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 340312
> 99.9%
Currency Symbol 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 103032
30.3%
C 81360
23.9%
X 64590
19.0%
T 39393
 
11.6%
G 16146
 
4.7%
O 5996
 
1.8%
L 4815
 
1.4%
K 4717
 
1.4%
Z 3159
 
0.9%
N 2789
 
0.8%
Other values (16) 14315
 
4.2%
Currency Symbol
ValueCountFrequency (%)
$ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 340312
> 99.9%
Common 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 103032
30.3%
C 81360
23.9%
X 64590
19.0%
T 39393
 
11.6%
G 16146
 
4.7%
O 5996
 
1.8%
L 4815
 
1.4%
K 4717
 
1.4%
Z 3159
 
0.9%
N 2789
 
0.8%
Other values (16) 14315
 
4.2%
Common
ValueCountFrequency (%)
$ 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 103032
30.3%
C 81360
23.9%
X 64590
19.0%
T 39393
 
11.6%
G 16146
 
4.7%
O 5996
 
1.8%
L 4815
 
1.4%
K 4717
 
1.4%
Z 3159
 
0.9%
N 2789
 
0.8%
Other values (17) 14324
 
4.2%

DESTINATION_ZIP
Unsupported

REJECTED  UNSUPPORTED 

Missing299
Missing (%)0.2%
Memory size1.3 MiB

IO_CODE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
74578 
2
50690 
4
31528 
3
14354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters171150
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

Length

2024-02-15T23:11:53.434691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T23:11:53.591351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

Most occurring characters

ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171150
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 171150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 74578
43.6%
2 50690
29.6%
4 31528
18.4%
3 14354
 
8.4%

MILEAGE
Real number (ℝ)

ZEROS 

Distinct2609
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean854.657
Minimum0
Maximum4814
Zeros37398
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:53.769429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median663
Q31448
95-th percentile2398
Maximum4814
Range4814
Interquartile range (IQR)1433

Descriptive statistics

Standard deviation873.58099
Coefficient of variation (CV)1.0221422
Kurtosis-0.69947766
Mean854.657
Median Absolute Deviation (MAD)663
Skewness0.68954862
Sum1.4627455 × 108
Variance763143.75
MonotonicityNot monotonic
2024-02-15T23:11:53.966784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37398
 
21.9%
1345 4034
 
2.4%
2118 2720
 
1.6%
117 2380
 
1.4%
15 2245
 
1.3%
1462 2142
 
1.3%
1024 2131
 
1.2%
1392 2029
 
1.2%
352 1933
 
1.1%
1102 1839
 
1.1%
Other values (2599) 112299
65.6%
ValueCountFrequency (%)
0 37398
21.9%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 963
 
0.6%
5 7
 
< 0.1%
6 81
 
< 0.1%
7 57
 
< 0.1%
8 361
 
0.2%
9 1592
 
0.9%
10 668
 
0.4%
ValueCountFrequency (%)
4814 1
 
< 0.1%
4653 1
 
< 0.1%
4651 6
< 0.1%
4541 1
 
< 0.1%
4432 1
 
< 0.1%
4418 4
< 0.1%
4394 1
 
< 0.1%
4359 2
 
< 0.1%
4332 1
 
< 0.1%
4327 6
< 0.1%
Distinct2105
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:54.217505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length8.3372714
Min length3

Characters and Unicode

Total characters1426924
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique675 ?
Unique (%)0.4%

Sample

1st rowLAS VEGAS
2nd rowLAS VEGAS
3rd rowHIDALGO
4th rowSAN DIEGO
5th rowCHULA VISTA
ValueCountFrequency (%)
san 28630
 
12.6%
diego 27461
 
12.1%
fairburn 17126
 
7.5%
desoto 11419
 
5.0%
tijuana 6774
 
3.0%
houston 5583
 
2.5%
mabank 5107
 
2.2%
south 4681
 
2.1%
gate 4307
 
1.9%
ontario 4190
 
1.8%
Other values (2139) 111778
49.2%
2024-02-15T23:11:54.667693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 162943
11.4%
N 148910
 
10.4%
O 144979
 
10.2%
E 123738
 
8.7%
I 102014
 
7.1%
R 95484
 
6.7%
S 84634
 
5.9%
T 69532
 
4.9%
D 62886
 
4.4%
56111
 
3.9%
Other values (29) 375693
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1370607
96.1%
Space Separator 56111
 
3.9%
Decimal Number 85
 
< 0.1%
Dash Punctuation 65
 
< 0.1%
Other Punctuation 29
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 162943
11.9%
N 148910
10.9%
O 144979
10.6%
E 123738
 
9.0%
I 102014
 
7.4%
R 95484
 
7.0%
S 84634
 
6.2%
T 69532
 
5.1%
D 62886
 
4.6%
G 53182
 
3.9%
Other values (16) 322305
23.5%
Decimal Number
ValueCountFrequency (%)
1 34
40.0%
0 20
23.5%
3 15
17.6%
2 14
16.5%
6 2
 
2.4%
Other Punctuation
ValueCountFrequency (%)
/ 17
58.6%
, 5
 
17.2%
. 5
 
17.2%
" 2
 
6.9%
Space Separator
ValueCountFrequency (%)
56111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1370607
96.1%
Common 56317
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 162943
11.9%
N 148910
10.9%
O 144979
10.6%
E 123738
 
9.0%
I 102014
 
7.4%
R 95484
 
7.0%
S 84634
 
6.2%
T 69532
 
5.1%
D 62886
 
4.6%
G 53182
 
3.9%
Other values (16) 322305
23.5%
Common
ValueCountFrequency (%)
56111
99.6%
- 65
 
0.1%
1 34
 
0.1%
( 25
 
< 0.1%
0 20
 
< 0.1%
/ 17
 
< 0.1%
3 15
 
< 0.1%
2 14
 
< 0.1%
, 5
 
< 0.1%
. 5
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1426924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 162943
11.4%
N 148910
 
10.4%
O 144979
 
10.2%
E 123738
 
8.7%
I 102014
 
7.1%
R 95484
 
6.7%
S 84634
 
5.9%
T 69532
 
4.9%
D 62886
 
4.4%
56111
 
3.9%
Other values (29) 375693
26.3%
Distinct2445
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:54.981704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length12.282495
Min length5

Characters and Unicode

Total characters2102149
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique867 ?
Unique (%)0.5%

Sample

1st rowLAS VEGAS, NV
2nd rowLAS VEGAS, NV
3rd rowHIDALGO, TX
4th rowSAN DIEGO, CA
5th rowCHULA VISTA, CA
ValueCountFrequency (%)
ca 58498
 
14.8%
tx 33152
 
8.4%
san 28630
 
7.2%
diego 27461
 
7.0%
ga 20674
 
5.2%
fairburn 17126
 
4.3%
desoto 11419
 
2.9%
tijuana 6774
 
1.7%
houston 5583
 
1.4%
mabank 5107
 
1.3%
Other values (2222) 180657
45.7%
2024-02-15T23:11:55.471366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 253114
12.0%
224136
 
10.7%
, 171155
 
8.1%
N 159144
 
7.6%
O 158202
 
7.5%
E 124603
 
5.9%
I 109001
 
5.2%
T 105151
 
5.0%
R 96947
 
4.6%
C 92659
 
4.4%
Other values (29) 608037
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1706657
81.2%
Space Separator 224136
 
10.7%
Other Punctuation 171179
 
8.1%
Decimal Number 85
 
< 0.1%
Dash Punctuation 65
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 253114
14.8%
N 159144
 
9.3%
O 158202
 
9.3%
E 124603
 
7.3%
I 109001
 
6.4%
T 105151
 
6.2%
R 96947
 
5.7%
C 92659
 
5.4%
S 86747
 
5.1%
G 73881
 
4.3%
Other values (16) 447208
26.2%
Decimal Number
ValueCountFrequency (%)
1 34
40.0%
0 20
23.5%
3 15
17.6%
2 14
16.5%
6 2
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 171155
> 99.9%
/ 17
 
< 0.1%
. 5
 
< 0.1%
" 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
224136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1706657
81.2%
Common 395492
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 253114
14.8%
N 159144
 
9.3%
O 158202
 
9.3%
E 124603
 
7.3%
I 109001
 
6.4%
T 105151
 
6.2%
R 96947
 
5.7%
C 92659
 
5.4%
S 86747
 
5.1%
G 73881
 
4.3%
Other values (16) 447208
26.2%
Common
ValueCountFrequency (%)
224136
56.7%
, 171155
43.3%
- 65
 
< 0.1%
1 34
 
< 0.1%
( 25
 
< 0.1%
0 20
 
< 0.1%
/ 17
 
< 0.1%
3 15
 
< 0.1%
2 14
 
< 0.1%
. 5
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2102149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 253114
12.0%
224136
 
10.7%
, 171155
 
8.1%
N 159144
 
7.6%
O 158202
 
7.5%
E 124603
 
5.9%
I 109001
 
5.2%
T 105151
 
5.0%
R 96947
 
4.6%
C 92659
 
4.4%
Other values (29) 608037
28.9%
Distinct84
Distinct (%)< 0.1%
Missing926
Missing (%)0.5%
Memory size1.3 MiB
2024-02-15T23:11:55.647794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters340448
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 152327
89.5%
mx 7611
 
4.5%
ca 3832
 
2.3%
cz 1347
 
0.8%
de 483
 
0.3%
jp 477
 
0.3%
tr 455
 
0.3%
it 424
 
0.2%
gb 424
 
0.2%
se 277
 
0.2%
Other values (74) 2567
 
1.5%
2024-02-15T23:11:55.968878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 152749
44.9%
U 152702
44.9%
M 7749
 
2.3%
X 7613
 
2.2%
C 5412
 
1.6%
A 4291
 
1.3%
Z 1353
 
0.4%
T 1180
 
0.3%
E 985
 
0.3%
I 940
 
0.3%
Other values (16) 5474
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 340448
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 152749
44.9%
U 152702
44.9%
M 7749
 
2.3%
X 7613
 
2.2%
C 5412
 
1.6%
A 4291
 
1.3%
Z 1353
 
0.4%
T 1180
 
0.3%
E 985
 
0.3%
I 940
 
0.3%
Other values (16) 5474
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 340448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 152749
44.9%
U 152702
44.9%
M 7749
 
2.3%
X 7613
 
2.2%
C 5412
 
1.6%
A 4291
 
1.3%
Z 1353
 
0.4%
T 1180
 
0.3%
E 985
 
0.3%
I 940
 
0.3%
Other values (16) 5474
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 152749
44.9%
U 152702
44.9%
M 7749
 
2.3%
X 7613
 
2.2%
C 5412
 
1.6%
A 4291
 
1.3%
Z 1353
 
0.4%
T 1180
 
0.3%
E 985
 
0.3%
I 940
 
0.3%
Other values (16) 5474
 
1.6%
Distinct5609
Distinct (%)3.3%
Missing1
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-15T23:11:56.367893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length13.068402
Min length7

Characters and Unicode

Total characters2236644
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2801 ?
Unique (%)1.6%

Sample

1st rowMelissa Bowen
2nd rowMelissa Bowen
3rd rowDana Hancock
4th rowAshley Rocha
5th rowJessica Huang
ValueCountFrequency (%)
nguyen 16564
 
4.8%
kristie 16504
 
4.8%
sanchez 11141
 
3.2%
roy 11113
 
3.2%
gregory 9930
 
2.9%
avery 9687
 
2.8%
scott 8135
 
2.3%
larsen 6676
 
1.9%
hailey 6674
 
1.9%
angela 6047
 
1.7%
Other values (1481) 244564
70.5%
2024-02-15T23:11:56.660479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 224761
 
10.0%
175886
 
7.9%
r 171547
 
7.7%
a 169884
 
7.6%
n 157454
 
7.0%
i 135040
 
6.0%
o 120240
 
5.4%
l 95353
 
4.3%
s 94896
 
4.2%
y 92132
 
4.1%
Other values (44) 799451
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1708226
76.4%
Uppercase Letter 350489
 
15.7%
Space Separator 175886
 
7.9%
Other Punctuation 2043
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 224761
13.2%
r 171547
10.0%
a 169884
9.9%
n 157454
9.2%
i 135040
 
7.9%
o 120240
 
7.0%
l 95353
 
5.6%
s 94896
 
5.6%
y 92132
 
5.4%
t 87530
 
5.1%
Other values (16) 359389
21.0%
Uppercase Letter
ValueCountFrequency (%)
S 37826
 
10.8%
A 32219
 
9.2%
C 25359
 
7.2%
K 23953
 
6.8%
R 23335
 
6.7%
M 22526
 
6.4%
N 21838
 
6.2%
L 20125
 
5.7%
H 18354
 
5.2%
B 17074
 
4.9%
Other values (16) 107880
30.8%
Space Separator
ValueCountFrequency (%)
175886
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2058715
92.0%
Common 177929
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 224761
 
10.9%
r 171547
 
8.3%
a 169884
 
8.3%
n 157454
 
7.6%
i 135040
 
6.6%
o 120240
 
5.8%
l 95353
 
4.6%
s 94896
 
4.6%
y 92132
 
4.5%
t 87530
 
4.3%
Other values (42) 709878
34.5%
Common
ValueCountFrequency (%)
175886
98.9%
. 2043
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2236644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 224761
 
10.0%
175886
 
7.9%
r 171547
 
7.7%
a 169884
 
7.6%
n 157454
 
7.0%
i 135040
 
6.0%
o 120240
 
5.4%
l 95353
 
4.3%
s 94896
 
4.2%
y 92132
 
4.1%
Other values (44) 799451
35.7%

ORIGIN_STATE
Text

MISSING 

Distinct101
Distinct (%)0.1%
Missing3125
Missing (%)1.8%
Memory size1.3 MiB
2024-02-15T23:11:56.792481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters336050
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowNV
2nd rowNV
3rd rowTX
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 58497
34.8%
tx 33152
19.7%
ga 20674
 
12.3%
oh 4626
 
2.8%
xx 4502
 
2.7%
ok 4401
 
2.6%
pa 3695
 
2.2%
bc 3612
 
2.1%
az 3490
 
2.1%
wi 3270
 
1.9%
Other values (91) 28106
16.7%
2024-02-15T23:11:57.018200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 90171
26.8%
C 66419
19.8%
X 45302
13.5%
T 35619
 
10.6%
G 20699
 
6.2%
O 13223
 
3.9%
N 10234
 
3.0%
M 8317
 
2.5%
I 6987
 
2.1%
K 6454
 
1.9%
Other values (16) 32625
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 336050
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 90171
26.8%
C 66419
19.8%
X 45302
13.5%
T 35619
 
10.6%
G 20699
 
6.2%
O 13223
 
3.9%
N 10234
 
3.0%
M 8317
 
2.5%
I 6987
 
2.1%
K 6454
 
1.9%
Other values (16) 32625
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 336050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 90171
26.8%
C 66419
19.8%
X 45302
13.5%
T 35619
 
10.6%
G 20699
 
6.2%
O 13223
 
3.9%
N 10234
 
3.0%
M 8317
 
2.5%
I 6987
 
2.1%
K 6454
 
1.9%
Other values (16) 32625
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 90171
26.8%
C 66419
19.8%
X 45302
13.5%
T 35619
 
10.6%
G 20699
 
6.2%
O 13223
 
3.9%
N 10234
 
3.0%
M 8317
 
2.5%
I 6987
 
2.1%
K 6454
 
1.9%
Other values (16) 32625
 
9.7%

ORIGIN_ZIP
Unsupported

REJECTED  UNSUPPORTED 

Missing108
Missing (%)0.1%
Memory size1.3 MiB
Distinct1045
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2020-01-06 00:00:00
Maximum2024-02-06 00:00:00
2024-02-15T23:11:57.148741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:57.270021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PRO_NUMBER2
Real number (ℝ)

Distinct169730
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0124493 × 1012
Minimum1.2838423 × 108
Maximum9.9999377 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:57.393512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2838423 × 108
5-th percentile5.0447931 × 1011
Q12.5139144 × 1012
median5.0167654 × 1012
Q37.5063477 × 1012
95-th percentile9.5077653 × 1012
Maximum9.9999377 × 1012
Range9.9998093 × 1012
Interquartile range (IQR)4.9924333 × 1012

Descriptive statistics

Standard deviation2.8867893 × 1012
Coefficient of variation (CV)0.57592389
Kurtosis-1.1981616
Mean5.0124493 × 1012
Median Absolute Deviation (MAD)2.4965018 × 1012
Skewness-0.0040224445
Sum8.578807 × 1017
Variance8.3335526 × 1024
MonotonicityNot monotonic
2024-02-15T23:11:57.521819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.345975234 × 10124
 
< 0.1%
2.244318968 × 10124
 
< 0.1%
8.605623023 × 10123
 
< 0.1%
3.145539651 × 10123
 
< 0.1%
1.378475192 × 10113
 
< 0.1%
9.522002647 × 10123
 
< 0.1%
7.666873734 × 10123
 
< 0.1%
7.867134112 × 10113
 
< 0.1%
7.940553501 × 10123
 
< 0.1%
4.22110682 × 10113
 
< 0.1%
Other values (169720) 171118
> 99.9%
ValueCountFrequency (%)
128384228 1
< 0.1%
135149629 1
< 0.1%
169549877 1
< 0.1%
175591785 1
< 0.1%
213976710 1
< 0.1%
402685065 1
< 0.1%
479960157 1
< 0.1%
497546968 1
< 0.1%
523138549 1
< 0.1%
553233603 1
< 0.1%
ValueCountFrequency (%)
9.999937663 × 10121
< 0.1%
9.999858228 × 10121
< 0.1%
9.999853088 × 10121
< 0.1%
9.999796206 × 10121
< 0.1%
9.999718983 × 10121
< 0.1%
9.999698537 × 10121
< 0.1%
9.999620617 × 10121
< 0.1%
9.999465428 × 10121
< 0.1%
9.999380799 × 10121
< 0.1%
9.999362346 × 10121
< 0.1%
Distinct5812
Distinct (%)3.4%
Missing191
Missing (%)0.1%
Memory size1.3 MiB
2024-02-15T23:11:57.700521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length16.219918
Min length1

Characters and Unicode

Total characters2772941
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3134 ?
Unique (%)1.8%

Sample

1st rowSOLAR TURBINES INC
2nd rowSOLAR TURBINES INC
3rd rowSOLAR TURBINES INC
4th rowSOLAR TURBINES INC
5th rowSOLAR TURBINES INC
ValueCountFrequency (%)
solar 97341
22.8%
turbines 94590
22.2%
harbo 18840
 
4.4%
kearn 17833
 
4.2%
inc 13208
 
3.1%
overh 9420
 
2.2%
turbotec 9041
 
2.1%
c/o 7174
 
1.7%
ups-scs 7109
 
1.7%
reman 6052
 
1.4%
Other values (4856) 146133
34.2%
2024-02-15T23:11:58.046170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 338878
12.2%
S 256446
9.2%
255863
9.2%
A 217788
 
7.9%
E 210968
 
7.6%
O 205280
 
7.4%
N 179548
 
6.5%
I 169465
 
6.1%
T 162584
 
5.9%
L 143040
 
5.2%
Other values (38) 633081
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2497877
90.1%
Space Separator 255863
 
9.2%
Dash Punctuation 9986
 
0.4%
Other Punctuation 8766
 
0.3%
Decimal Number 442
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 338878
13.6%
S 256446
10.3%
A 217788
8.7%
E 210968
8.4%
O 205280
8.2%
N 179548
 
7.2%
I 169465
 
6.8%
T 162584
 
6.5%
L 143040
 
5.7%
B 139241
 
5.6%
Other values (16) 474639
19.0%
Decimal Number
ValueCountFrequency (%)
2 203
45.9%
3 98
22.2%
0 42
 
9.5%
1 27
 
6.1%
8 23
 
5.2%
6 20
 
4.5%
4 10
 
2.3%
5 9
 
2.0%
7 6
 
1.4%
9 4
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 7569
86.3%
& 572
 
6.5%
% 569
 
6.5%
. 50
 
0.6%
' 3
 
< 0.1%
; 2
 
< 0.1%
@ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5
83.3%
[ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
255863
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9986
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2497877
90.1%
Common 275064
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 338878
13.6%
S 256446
10.3%
A 217788
8.7%
E 210968
8.4%
O 205280
8.2%
N 179548
 
7.2%
I 169465
 
6.8%
T 162584
 
6.5%
L 143040
 
5.7%
B 139241
 
5.6%
Other values (16) 474639
19.0%
Common
ValueCountFrequency (%)
255863
93.0%
- 9986
 
3.6%
/ 7569
 
2.8%
& 572
 
0.2%
% 569
 
0.2%
2 203
 
0.1%
3 98
 
< 0.1%
. 50
 
< 0.1%
0 42
 
< 0.1%
1 27
 
< 0.1%
Other values (12) 85
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2772941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 338878
12.2%
S 256446
9.2%
255863
9.2%
A 217788
 
7.9%
E 210968
 
7.6%
O 205280
 
7.4%
N 179548
 
6.5%
I 169465
 
6.1%
T 162584
 
5.9%
L 143040
 
5.2%
Other values (38) 633081
22.8%
Distinct5828
Distinct (%)3.4%
Missing1
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-15T23:11:58.258521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length15.707302
Min length2

Characters and Unicode

Total characters2688289
Distinct characters60
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3031 ?
Unique (%)1.8%

Sample

1st rowCALTROL INC
2nd rowCALTROL INC
3rd rowAMETEK-PRESTOLI
4th rowREXEL HOLDINGS
5th rowMOTION INDUSTRI
ValueCountFrequency (%)
solar 56207
 
13.6%
turbines 55779
 
13.5%
inc 21801
 
5.3%
overhaul 9685
 
2.3%
turbotec 8916
 
2.2%
mesa 6699
 
1.6%
kearny 6673
 
1.6%
chromalloy 5687
 
1.4%
c/o 5623
 
1.4%
ups-scs 5539
 
1.3%
Other values (4892) 231733
55.9%
2024-02-15T23:11:58.602499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 277560
 
10.3%
243357
 
9.1%
E 211734
 
7.9%
A 208665
 
7.8%
S 198820
 
7.4%
O 192331
 
7.2%
I 178820
 
6.7%
N 169526
 
6.3%
T 160962
 
6.0%
L 138895
 
5.2%
Other values (50) 707619
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2426060
90.2%
Space Separator 243357
 
9.1%
Other Punctuation 9202
 
0.3%
Dash Punctuation 9170
 
0.3%
Decimal Number 220
 
< 0.1%
Lowercase Letter 198
 
< 0.1%
Open Punctuation 42
 
< 0.1%
Close Punctuation 38
 
< 0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 277560
11.4%
E 211734
 
8.7%
A 208665
 
8.6%
S 198820
 
8.2%
O 192331
 
7.9%
I 178820
 
7.4%
N 169526
 
7.0%
T 160962
 
6.6%
L 138895
 
5.7%
U 122697
 
5.1%
Other values (16) 566050
23.3%
Lowercase Letter
ValueCountFrequency (%)
i 28
14.1%
a 28
14.1%
n 28
14.1%
l 16
8.1%
j 14
7.1%
o 14
7.1%
c 14
7.1%
m 14
7.1%
d 14
7.1%
r 14
7.1%
Decimal Number
ValueCountFrequency (%)
0 80
36.4%
2 39
17.7%
1 35
15.9%
3 27
 
12.3%
5 12
 
5.5%
6 8
 
3.6%
9 6
 
2.7%
4 6
 
2.7%
7 4
 
1.8%
8 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/ 7220
78.5%
& 1355
 
14.7%
. 364
 
4.0%
% 250
 
2.7%
# 4
 
< 0.1%
' 4
 
< 0.1%
; 4
 
< 0.1%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
243357
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2426258
90.3%
Common 262031
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 277560
11.4%
E 211734
 
8.7%
A 208665
 
8.6%
S 198820
 
8.2%
O 192331
 
7.9%
I 178820
 
7.4%
N 169526
 
7.0%
T 160962
 
6.6%
L 138895
 
5.7%
U 122697
 
5.1%
Other values (27) 566248
23.3%
Common
ValueCountFrequency (%)
243357
92.9%
- 9170
 
3.5%
/ 7220
 
2.8%
& 1355
 
0.5%
. 364
 
0.1%
% 250
 
0.1%
0 80
 
< 0.1%
( 42
 
< 0.1%
2 39
 
< 0.1%
) 38
 
< 0.1%
Other values (13) 116
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2688289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 277560
 
10.3%
243357
 
9.1%
E 211734
 
7.9%
A 208665
 
7.8%
S 198820
 
7.4%
O 192331
 
7.2%
I 178820
 
6.7%
N 169526
 
6.3%
T 160962
 
6.0%
L 138895
 
5.2%
Other values (50) 707619
26.3%
Distinct1476
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2020-01-01 00:00:00
Maximum2024-02-02 00:00:00
2024-02-15T23:11:58.750295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:58.905657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1476
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2020-01-01 00:00:00
Maximum2024-02-02 00:00:00
2024-02-15T23:11:59.060019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:59.205921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SHIP_WEIGHT
Real number (ℝ)

SKEWED  ZEROS 

Distinct14826
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3349.3079
Minimum0
Maximum1421500
Zeros19900
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-15T23:11:59.344548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1150
median465
Q31672.75
95-th percentile17805.1
Maximum1421500
Range1421500
Interquartile range (IQR)1522.75

Descriptive statistics

Standard deviation14571.031
Coefficient of variation (CV)4.3504604
Kurtosis2230.8997
Mean3349.3079
Median Absolute Deviation (MAD)459
Skewness34.111758
Sum5.7323405 × 108
Variance2.1231495 × 108
MonotonicityNot monotonic
2024-02-15T23:11:59.477965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19900
 
11.6%
1 3176
 
1.9%
100 1127
 
0.7%
300 1100
 
0.6%
200 1073
 
0.6%
400 965
 
0.6%
150 955
 
0.6%
1500 915
 
0.5%
500 885
 
0.5%
250 732
 
0.4%
Other values (14816) 140322
82.0%
ValueCountFrequency (%)
0 19900
11.6%
1 3176
 
1.9%
2 313
 
0.2%
2.205 468
 
0.3%
3 187
 
0.1%
4 136
 
0.1%
4.409 113
 
0.1%
5 164
 
0.1%
6 82
 
< 0.1%
6.614 56
 
< 0.1%
ValueCountFrequency (%)
1421500 1
 
< 0.1%
1178072.905 1
 
< 0.1%
1158465.326 1
 
< 0.1%
1158421.234 2
< 0.1%
973639 2
< 0.1%
971503.29 3
< 0.1%
672356.538 1
 
< 0.1%
663641.813 1
 
< 0.1%
532968 2
< 0.1%
527277.208 1
 
< 0.1%

SOLAR_MODE
Categorical

Distinct12
Distinct (%)< 0.1%
Missing311
Missing (%)0.2%
Memory size1.3 MiB
LTL
106381 
TL
23510 
AE
17099 
FF
 
7548
CU
 
6377
Other values (7)
 
9924

Length

Max length3
Median length3
Mean length2.6327888
Min length2

Characters and Unicode

Total characters449783
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLTL
2nd rowLTL
3rd rowLTL
4th rowLTL
5th rowLTL

Common Values

ValueCountFrequency (%)
LTL 106381
62.2%
TL 23510
 
13.7%
AE 17099
 
10.0%
FF 7548
 
4.4%
CU 6377
 
3.7%
HH 5700
 
3.3%
CR 2016
 
1.2%
OCN 1548
 
0.9%
MS 372
 
0.2%
PAR 176
 
0.1%
Other values (2) 112
 
0.1%
(Missing) 311
 
0.2%

Length

2024-02-15T23:11:59.610852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltl 106381
62.3%
tl 23510
 
13.8%
ae 17099
 
10.0%
ff 7548
 
4.4%
cu 6377
 
3.7%
hh 5700
 
3.3%
cr 2016
 
1.2%
ocn 1548
 
0.9%
ms 372
 
0.2%
par 176
 
0.1%
Other values (2) 112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 236272
52.5%
T 129891
28.9%
A 17347
 
3.9%
E 17099
 
3.8%
F 15096
 
3.4%
H 11400
 
2.5%
C 9941
 
2.2%
U 6377
 
1.4%
R 2192
 
0.5%
O 1548
 
0.3%
Other values (5) 2620
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 449783
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 236272
52.5%
T 129891
28.9%
A 17347
 
3.9%
E 17099
 
3.8%
F 15096
 
3.4%
H 11400
 
2.5%
C 9941
 
2.2%
U 6377
 
1.4%
R 2192
 
0.5%
O 1548
 
0.3%
Other values (5) 2620
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 449783
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 236272
52.5%
T 129891
28.9%
A 17347
 
3.9%
E 17099
 
3.8%
F 15096
 
3.4%
H 11400
 
2.5%
C 9941
 
2.2%
U 6377
 
1.4%
R 2192
 
0.5%
O 1548
 
0.3%
Other values (5) 2620
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 449783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 236272
52.5%
T 129891
28.9%
A 17347
 
3.9%
E 17099
 
3.8%
F 15096
 
3.4%
H 11400
 
2.5%
C 9941
 
2.2%
U 6377
 
1.4%
R 2192
 
0.5%
O 1548
 
0.3%
Other values (5) 2620
 
0.6%

Interactions

2024-02-15T23:11:46.097764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:41.518023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.653003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:43.718332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.507041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.319888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:46.237488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:41.711304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.830686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:43.897338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.633768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.459732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:46.371146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:41.884659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.990441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.055673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.774528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.583138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:46.632371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.063068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:43.173634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.167160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.917257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.709866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:46.781637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.264712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:43.340075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.269365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.045964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.836228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:46.939661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:42.447780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:43.524583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:44.378200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.178300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T23:11:45.964983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-15T23:11:59.686325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACCESSORIAL_AMOUNTAMOUNT_PAIDIO_CODEMILEAGEPRO_NUMBER2SHIP_WEIGHTSOLAR_MODEUnnamed: 0
ACCESSORIAL_AMOUNT1.0000.5950.011-0.184-0.0030.0990.0450.105
AMOUNT_PAID0.5951.0000.017-0.013-0.0030.4130.0460.149
IO_CODE0.0110.0171.000-0.081-0.0040.0170.3110.880
MILEAGE-0.184-0.013-0.0811.0000.0060.2930.147-0.085
PRO_NUMBER2-0.003-0.003-0.0040.0061.0000.0000.000-0.003
SHIP_WEIGHT0.0990.4130.0170.2930.0001.0000.0600.033
SOLAR_MODE0.0450.0460.3110.1470.0000.0601.0000.039
Unnamed: 00.1050.1490.880-0.085-0.0030.0330.0391.000

Missing values

2024-02-15T23:11:47.387887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-15T23:11:48.189368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-15T23:11:49.379852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0ACCESSORIAL_AMOUNTAMOUNT_PAIDCARRIER_NAME2DESTINATION_CITYDESTINATION_CITY_STATEDESTINATION_COUNTRY_CODEDESTINATION_NAME2DESTINATION_STATEDESTINATION_ZIPIO_CODEMILEAGEORIGIN_CITYORIGIN_CITY_STATEORIGIN_COUNTRY_CODEORIGIN_NAME2ORIGIN_STATEORIGIN_ZIPPROCESS_DAY_YEARPRO_NUMBER2RECEIVERSHIPPERSHIP_DATESHIP_DAY_YEARSHIP_WEIGHTSOLAR_MODE
0023.06070121.58442Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021311970LAS VEGASLAS VEGAS, NVUSMelissa BowenNV891152023-10-163490944617186SOLAR TURBINES INCCALTROL INC2023-10-052023-10-05210.0LTL
1123.06070121.58442Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021311970LAS VEGASLAS VEGAS, NVUSMelissa BowenNV891152023-10-16122517075583SOLAR TURBINES INCCALTROL INC2023-10-052023-10-0570.0LTL
2227.73854146.27448Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021311127HIDALGOHIDALGO, TXUSDana HancockTX785572023-10-167453824345995SOLAR TURBINES INCAMETEK-PRESTOLI2023-10-062023-10-06218.0LTL
3325.89894136.55088Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021312118SAN DIEGOSAN DIEGO, CAUSAshley RochaCA921542023-10-163661919417937SOLAR TURBINES INCREXEL HOLDINGS2023-10-042023-10-0465.0LTL
4468.28858358.90596Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021312124CHULA VISTACHULA VISTA, CAUSJessica HuangCA919142023-10-247220791461559SOLAR TURBINES INCMOTION INDUSTRI2023-10-122023-10-12775.0LTL
55133.73892702.85860Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021312149ORANGEORANGE, CAUSLindsay FrenchCA928652023-10-243757223470581SOLAR TURBINES INCBERENDSEN FLUID2023-10-132023-10-132120.0LTL
6625.45218136.10412Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021312144SAN MARCOSSAN MARCOS, CAUSDavid LevyCA920692023-10-255300955865408SOLAR TURBINES INCWINCHESTER INTE2023-10-162023-10-16191.0LTL
7723.32350124.72488Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA3021311013ELLSWORTHELLSWORTH, KSUSLinda MoralesKS674392023-10-256793434429403SOLAR TURBINES INCCASHCO2023-10-162023-10-16320.0LTL
8839.80106212.85486Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA302131695BRIDGEVIEWBRIDGEVIEW, ILUSLinda HornIL604552023-10-253565251704437SOLAR TURBINES INCNORMAN EQUIPMEN2023-10-172023-10-17650.0LTL
9923.32350124.75116Hodges, Lyons and WeaverFAIRBURNFAIRBURN, GAUSMichael ClarkGA302131903CEDAR FALLSCEDAR FALLS, IAUSDillon WhiteIA506132023-10-251836265927986SOLAR TURBINES INCVIKING PUMP2023-10-172023-10-1770.0LTL
Unnamed: 0ACCESSORIAL_AMOUNTAMOUNT_PAIDCARRIER_NAME2DESTINATION_CITYDESTINATION_CITY_STATEDESTINATION_COUNTRY_CODEDESTINATION_NAME2DESTINATION_STATEDESTINATION_ZIPIO_CODEMILEAGEORIGIN_CITYORIGIN_CITY_STATEORIGIN_COUNTRY_CODEORIGIN_NAME2ORIGIN_STATEORIGIN_ZIPPROCESS_DAY_YEARPRO_NUMBER2RECEIVERSHIPPERSHIP_DATESHIP_DAY_YEARSHIP_WEIGHTSOLAR_MODE
17114017114060.16806330.83892Pham LLCCARROLLTONCARROLLTON, TXUSNicole BanksTX7500641649NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732022-03-023201705855910AMERICAN CRATINSAFT AMERICA IN2022-02-162022-02-16628.0LTL
171141171141257.438881475.28036Pham LLCCARSONCARSON, CAUSLance ThompsonCA9074542880NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732022-03-155283350332990CAL COAST PACKISAFT AMERICA IN2022-03-022022-03-022929.0LTL
17114217114233.02082163.29078Pham LLCFAIRBURNFAIRBURN, GAUSChristopher ShepardGA302134995NORTH HAVENNORTH HAVEN, CTUSKevin JenningsCT64732022-03-085031287741664SOLAR TURBINESALCAD BATTERIES2022-02-282022-02-2895.0LTL
171143171143539.436422732.27904Pham LLCCARSONCARSON, CAUSLance ThompsonCA9074542880NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732022-03-258022334641478CAL COAST PACKISAFT AMERICA IN2022-03-152022-03-156066.0LTL
171144171144125.80236784.52370Pham LLCCARROLLTONCARROLLTON, TXUSNicole BanksTX7500641649NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732022-01-257056781576015AMERICAN CRATINSAFT AMERICA IN2022-01-102022-01-102062.0LTL
17114517114543.54596373.67532Pham LLCONTARIOONTARIO, CAUSChristopher ShepardCA9176142931ASHLANDASHLAND, MAUSTracy WilsonMA17212021-05-105614587526058SOLAR TURBINESKIDDE FENWAL2020-06-182020-06-18655.0LTL
171146171146252.379982114.60706Pham LLCCARSONCARSON, CAUSLance ThompsonCA9074542863NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732021-05-072622240063621CAL COAST PACKISAFT AMERICA IN2021-01-182021-01-186176.0LTL
17114717114751.81102367.73604Pham LLCHOUSTONHOUSTON, TXUSKimberly WalkerTX7707341681NORTH HAVENNORTH HAVEN, CTUSBrenda DrakeCT64732021-05-073668604150049SANTINI EXPORTSAFT AMERICA IN2021-03-262021-03-26725.0LTL
17114817114825.18938244.25946Pham LLCSAN DIEGOSAN DIEGO, CAUSChristopher ShepardCA9215442341KINGS MOUNTAINKINGS MOUNTAIN, NCUSMelinda BarkerNC280862021-05-074389720540857SOLAR TURBINESPARKER HANNIFIN2020-09-032020-09-03463.0LTL
171149171149154.78920509.20128Pham LLCFAIRBURNFAIRBURN, GAUSChristopher ShepardGA302134995NORTH HAVENNORTH HAVEN, CTUSKevin JenningsCT64732022-02-097530857275397SOLAR TURBINESALCAD BATTERIES2022-01-272022-01-27491.0LTL